Evidence map›Paper›PMID 40596720›Full record

ArticleScientific reports2025

QTL mapping for yield contributing traits in mungbean (Vigna radiata L.) using a RIL population.

Shashidhar B Reddappa, Muralidhar S Aski, Gyan P Mishra, Soma Gupta, Kumbarahally M Shivaprasad, Subodh K Sinha, Ranjeet R Kumar, Anirban Roy, Dwijesh C Mishra, Shouvik Das and 3 more

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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5 · Who and what money

Authors and funding

13 authors.

Shashidhar B Reddappa *Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India.
Muralidhar S Aski *Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India.
Gyan P MishraDivision of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India. gyan.gene@gmail.com.
Soma GuptaDivision of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India.
Kumbarahally M ShivaprasadDivision of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India.
Subodh K SinhaNational Institute for Plant Biotechnology, New Delhi, 110012, India.
Ranjeet R KumarDivision of Biochemistry, Indian Agricultural Research Institute, New Delhi, 110012, India.
Anirban RoyDivision of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012, India.
Dwijesh C MishraAgricultural Bioinformatics, Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
Shouvik DasLaboratory of Plant Cell Wall Biology, Regional Centre for Biotechnology, Faridabad, 121001, Haryana, India.
Prashant Anupama Mohan PawarLaboratory of Plant Cell Wall Biology, Regional Centre for Biotechnology, Faridabad, 121001, Haryana, India.
Ramakrishnan Madhavan NairWorld Vegetable Center, South Asia, ICRISAT Campus Patancheru, Hyderabad, 502324, India.
Harsh K DikshitDivision of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India. harshgeneticsiari@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mungbean (Vigna radiata L.) is one of the most important yet genomically under-researched leguminous food crop. Its productivity is low due to the complex nature of yield realization, which is regulated by various yield-contributing traits. Thus, understanding the genetic basis of these traits is essential for developing an ideal genotype with high yield. In this study, mapping of quantitative trait loci (QTLs) was conducted for six yield-contributing traits using a recombinant inbred line (RIL) population (166 number), developed by crossing two contrasting genotypes (Pusa Baisakhi × PMR-1). The genotyping-by-sequencing (GBS) of RILs was used to construct the genetic map using 1347 single nucleotide polymorphism (SNPs). The QTL mapping using multiple interval mapping (MIM) and composite interval mapping (CIM) has identified 17 yield contributing QTLs of which, 4 for number of leaves/plant (NL), 3 for plant height (PH), 2 for SPAD value, 3 for 100 seed weight (SW), 2 for number of pods/plant (NP), and 3 for total grain yield (GY). The Logarithm of Odds (LOD) scores for these QTLs ranged from ~ 3-9, while phenotypic variance explained (PVE) ranged from ~ 9-24%. Several candidate genes with mRNA expression and protein-altering mutations were identified as having a direct role in key processes like growth (LOC106756212, LOC106776425, LOC106777991), flowering (LOC106777903, LOC106768860), metabolism (LOC106757749, LOC106758189), etc. The candidate genes are validated through digital gene expression analysis. In addition, Insertion-Deletion (InDel) markers were also developed for the identified QTLs which hold broad applications for the improvement of yield-related traits in mungbean.

Indexed as

Chromosome MappingQuantitative Trait LociVignaChromosomes, PlantGenotypePhenotypePolymorphism, Single NucleotideQuantitative Trait, HeritableCandidate genesComposite interval mappingGenotyping by sequencingMultiple interval mappingYield traits

Identifiers

PMID40596720
PMCPMC12218555

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